Experimental approaches to identify non-coding RNAs

Non-coding RNAs, a double-edged sword in breast cancer prognosis

Cancer is a rising issue worldwide, and numerous studies have focused on understanding the underlying reasons for its occurrence and finding proper ways to defeat it. By applying technological advances, researchers are continuously uncovering and updating treatments in cancer therapy. Their vast functions in the regulation of cell growth and proliferation and their significant role in the progression of diseases, including cancer. This review provides a comprehensive analysis of ncRNAs in breast cancer, focusing on long non-coding RNAs such as HOTAIR, MALAT1, and NEAT1, as well as microRNAs such as miR-21, miR-221/222, and miR-155. These ncRNAs are pivotal in regulating cell proliferation, metastasis, drug resistance, and apoptosis. Additionally, we discuss experimental approaches that are useful for studying them and highlight the advantages and challenges of each method. We then explain the results of these clinical trials and offer insights for future studies by discussing major existing gaps. On the basis of an extensive number of studies, this review provides valuable insights into the potential of ncRNAs in cancer therapy. Key findings show that even though the functions of ncRNAs are vast and undeniable in cancer, there are still complications associated with their therapeutic use. Moreover, there is an absence of sufficient experiments regarding their application in mouse models, which is an area to work on. By emphasizing the crucial role of ncRNAs, this review underscores the need for innovative approaches and further studies to explore their potential in cancer therapy.

Non-Coding RNAs in Cancer: Structure, Function, and Clinical Application

We are on the brink of a paradigm shift in both theoretical and clinical oncology. Genomic and transcriptomic profiling, alongside personalized approaches that account for individual patient variability, are increasingly shaping discourse. Discussions on the future of personalized cancer medicine are mainly dominated by the potential of non-coding RNAs (ncRNAs), which play a prominent role in cancer progression and metastasis formation by regulating the expression of oncogenic or tumor suppressor proteins at transcriptional and post-transcriptional levels; furthermore, their cell-free counterparts might be involved in intercellular communication. Non-coding RNAs are considered to be promising biomarker candidates for early diagnosis of cancer as well as potential therapeutic agents. This review aims to provide clarity amidst the vast body of literature by focusing on diverse species of ncRNAs, exploring the structure, origin, function, and potential clinical applications of miRNAs, siRNAs, lncRNAs, circRNAs, snRNAs, snoRNAs, eRNAs, paRNAs, YRNAs, vtRNAs, and piRNAs. We discuss molecular methods used for their detection or functional studies both in vitro and in vivo. We also address the challenges that must be overcome to enter a new era of cancer diagnosis and therapy that will reshape the future of oncology.

Genetic and epigenetic regulation of non-coding RNAs: Implications in cancer metastasis, stemness and drug resistance

Cancer stem cells (CSCs) have a crucial function in the initiation, advancement, and resistance to therapy of tumors. Recent findings indicate that non-coding RNAs (ncRNAs), such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), play a complex role in controlling the features of cancer stem cells (CSCs). Non-coding RNAs (ncRNAs) play a crucial role in controlling important characteristics of stem cells, such as their ability to renew themselves, differentiate into distinct cell types, and resist therapy. This article provides an overview of the current understanding of the complex relationship between non-coding RNAs (ncRNAs), namely microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), and cancer stem cells (CSCs). Particular microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are involved in regulating important signaling pathways like as Wnt, Notch, and Hedgehog, which control stem cell-like characteristics. The miR-34, miR-200, and let-7 families specifically aim at inhibiting the process of self-renewal and epithelial-to-mesenchymal transition. On the other hand, long non-coding RNAs (lncRNAs) such as H19, HOTAIR, and MALAT1 play a role in modifying the epigenetic landscape, hence enhancing the characteristics of stemness. This article also offers a thorough examination of the role of non-coding RNAs (ncRNAs) in regulating cancer stemness, emphasizing their impact on crucial biochemical pathways, epigenetic changes, and therapeutic implications. Comprehending the interaction between non-coding RNAs (ncRNAs) and cancer stem cells (CSCs) provides fresh perspectives on possible focused treatments for fighting aggressive and resistant malignancies. Gaining a comprehensive understanding of the connection between non-coding RNA (ncRNA) and cancer stem cells (CSC) offers valuable insights for the development of novel and precise treatments to combat aggressive cancers that are resistant to conventional therapies. In addition, the combination of ncRNA therapies with conventional methods like as chemotherapy or epigenetic medicines could result in synergistic effects. Nevertheless, there are still obstacles to overcome in terms of delivery, effectiveness, and safety. In summary, the interaction between non-coding RNA and cancer stemness shows potential as a targeted treatment approach in the field of precision oncology. This calls for additional investigation and use in clinical settings.

An sRNA overexpression library reveals AbnZ as a negative regulator of an essential translocation module in Caulobacter crescentus

Small RNAs (sRNAs) play a crucial role in modulating target gene expression through short base-pairing interactions and serve as integral components of many stress response pathways and regulatory circuits in bacteria. Transcriptome analyses have facilitated the annotation of dozens of sRNA candidates in the ubiquitous environmental model bacterium Caulobacter crescentus, but their physiological functions have not been systematically investigated so far. To address this gap, we have established CauloSOEP, a multi-copy plasmid library of C. crescentus sRNAs, which can be studied in a chosen genetic background and under select conditions. Demonstrating the power of CauloSOEP, we identified sRNA AbnZ to impair cell viability and morphology. AbnZ is processed from the 3' end of the polycistronic abn mRNA encoding the tripartite envelope-spanning efflux pump AcrAB-NodT. A combinatorial approach revealed the essential membrane translocation module TamAB as a target of AbnZ, implying that growth inhibition by AbnZ is linked to repression of this system.

Genome-Wide Development and Characterization of Microsatellite Markers in the Great Web-Spinning Sawfly Acantholyda posticalis

The great web-spinning sawfly Acantholyda posticalis is notorious for damaging Pinus forests across the Palearctic region. At present, uncertainties persist regarding its intraspecies variation and presumed subspecies. To use as tools for future studies, herein we developed genome-wide microsatellite markers for A. posticalis. Through searching, rigorous manual screening, and amplification trial, 56 microsatellite markers were obtained from the genome sequences. We characterized these markers across two populations from Shandong province (SD) and Heilongjiang province (HLJ) in China, and carried out cross-amplification in three related species. Out of the 56 markers tested, 10, 31, and 15 were categorized into high, moderate, and low polymorphic levels, respectively, based on their polymorphic information content (PIC) values. Meanwhile, 28, 19, and 4 microsatellite loci were successfully cross-amplified in Cephalcia yanqingensis, C. chuxiongica, and C. infumata, respectively, which could serve as potential molecular markers for their further studies. STRUCTURE and PCoA analyses revealed two distinct clusters corresponding to SD and HLJ, respectively, indicating a high resolution of these markers. Therefore，the 56 microsatellite markers identified here have the potential to serve as efficient tools for unraveling intraspecies variation and evolutionary history of A. posticalis.

MFPINC: prediction of plant ncRNAs based on multi-source feature fusion

Non-coding RNAs (ncRNAs) are recognized as pivotal players in the regulation of essential physiological processes such as nutrient homeostasis, development, and stress responses in plants. Common methods for predicting ncRNAs are susceptible to significant effects of experimental conditions and computational methods, resulting in the need for significant investment of time and resources. Therefore, we constructed an ncRNA predictor(MFPINC), to predict potential ncRNA in plants which is based on the PINC tool proposed by our previous studies. Specifically, sequence features were carefully refined using variance thresholding and F-test methods, while deep features were extracted and feature fusion were performed by applying the GRU model. The comprehensive evaluation of multiple standard datasets shows that MFPINC not only achieves more comprehensive and accurate identification of gene sequences, but also significantly improves the expressive and generalization performance of the model, and MFPINC significantly outperforms the existing competing methods in ncRNA identification. In addition, it is worth mentioning that our tool can also be found on Github ( https://github.com/Zhenj-Nie/MFPINC ) the data and source code can also be downloaded for free.

From tradition to innovation: conventional and deep learning frameworks in genome annotation

Following the milestone success of the Human Genome Project, the 'Encyclopedia of DNA Elements (ENCODE)' initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the emergence of numerous novel technologies, accompanied by the provision of vast amounts of whole-genome sequences, high-throughput data such as ChIP-Seq and RNA-Seq. Extracting biologically meaningful information from this massive dataset has become a critical aspect of many recent studies, particularly in annotating and predicting the functions of unknown genes. The core idea behind genome annotation is to identify genes and various functional elements within the genome sequence and infer their biological functions. Traditional wet-lab experimental methods still rely on extensive efforts for functional verification. However, early bioinformatics algorithms and software primarily employed shallow learning techniques; thus, the ability to characterize data and features learning was limited. With the widespread adoption of RNA-Seq technology, scientists from the biological community began to harness the potential of machine learning and deep learning approaches for gene structure prediction and functional annotation. In this context, we reviewed both conventional methods and contemporary deep learning frameworks, and highlighted novel perspectives on the challenges arising during annotation underscoring the dynamic nature of this evolving scientific landscape.

The Emerging Role of Non-Coding RNAs (ncRNAs) in Plant Growth, Development, and Stress Response Signaling

Plant species utilize a variety of regulatory mechanisms to ensure sustainable productivity. Within this intricate framework, numerous non-coding RNAs (ncRNAs) play a crucial regulatory role in plant biology, surpassing the essential functions of RNA molecules as messengers, ribosomal, and transfer RNAs. ncRNAs represent an emerging class of regulators, operating directly in the form of small interfering RNAs (siRNAs), microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs). These ncRNAs exert control at various levels, including transcription, post-transcription, translation, and epigenetic. Furthermore, they interact with each other, contributing to a variety of biological processes and mechanisms associated with stress resilience. This review primarily concentrates on the recent advancements in plant ncRNAs, delineating their functions in growth and development across various organs such as root, leaf, seed/endosperm, and seed nutrient development. Additionally, this review broadens its scope by examining the role of ncRNAs in response to environmental stresses such as drought, salt, flood, heat, and cold in plants. This compilation offers updated information and insights to guide the characterization of the potential functions of ncRNAs in plant growth, development, and stress resilience in future research.

Non-coding RNAs as potential therapeutic targets for receptor tyrosine kinase signaling in solid tumors: current status and future directions

This review article presents an in-depth analysis of the current state of research on receptor tyrosine kinase regulatory non-coding RNAs (RTK-RNAs) in solid tumors. RTK-RNAs belong to a class of non-coding RNAs (nc-RNAs) responsible for regulating the expression and activity of receptor tyrosine kinases (RTKs), which play a critical role in cancer development and progression. The article explores the molecular mechanisms through which RTK-RNAs modulate RTK signaling pathways and highlights recent advancements in the field. This include the identification of potential new RTK-RNAs and development of therapeutic strategies targeting RTK-RNAs. While the review discusses promising results from a variety of studies, encompassing in vitro, in vivo, and clinical investigations, it is important to acknowledge the challenges and limitations associated with targeting RTK-RNAs for therapeutic applications. Further studies involving various cancer cell lines, animal models, and ultimately, patients are necessary to validate the efficacy of targeting RTK-RNAs. The specificity of ncRNAs in targeting cellular pathways grants them tremendous potential, but careful consideration is required to minimize off-target effects, the article additionally discusses the potential clinical applications of RTK-RNAs as biomarkers for cancer diagnosis, prognosis, and treatment. In essence, by providing a comprehensive overview of the current understanding of RTK-RNAs in solid tumors, this review emphasizes their potential as therapeutic targets for cancer while acknowledging the associated challenges and limitations.

Tumor circulating biomarkers in colorectal cancer

CRC is a major global health concern and is responsible for a significant number of cancer-related deaths each year. The successful treatment of CRC becomes more difficult when it goes undetected until it has advanced to a later stage. Diagnostic biomarkers can play a critical role in the early detection of CRC, which leads to improved patient outcomes and increased survival rates. It is important to develop reliable biomarkers for the early detection of CRC to enable timely diagnosis and treatment. To date, CRC detection methods such as endoscopy, blood, and stool tests are imperfect and often only identify cases in the later stages of the disease. To overcome these limitations, researchers are turning to molecular biomarkers as a promising avenue for improving CRC detection. Diagnostic information can be provided more reliably through a noninvasive approach using biomarkers such as mRNA, circulating cell-free DNA, micro-RNA, long non-coding RNA, and proteins. These biomarkers can be found in blood, tissue, feces, and volatile organic compounds. The identification of molecular biomarkers with high sensitivity and specificity for early detection of CRC that are safe, cost-effective, and easily measurable remains a significant challenge for researchers. In this article, we will explore the latest advancements in blood-based diagnostic biomarkers for CRC and their potential impact on improving patient survival rates.

The role of microRNAs in the pathophysiology of human central nervous system: A focus on neurodegenerative diseases

microRNAs (miRNAs) are suggested to play substantial roles in regulating the development and various physiologic functions of the central nervous system (CNS). These include neurogenesis, cell fate and differentiation, morphogenesis, formation of dendrites, and targeting non-neural mRNAs. Notably, deregulation of an increasing number of miRNAs is associated with several neurodegenerative diseases including Alzheimer's disease, Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis and CNS tumors. They are particularly known to affect the amyloid β (Aβ) cleavage and accumulation, tau protein homeostasis, and expression of alpha-synuclein (α-syn), Parkin, PINK1, and brain-derived neurotrophic factor (BDNF) that play pivotal roles in the pathogenesis of neurodegenerative diseases. These include miR-16, miR-17-5p, miR-20a, miR-106a, miR-106b, miR-15a, miR-15b, miR-103, miR-107, miR-298, miR-328, miR-195, miR-485, and miR-29. In CNS tumors, several miRNAs, including miR-31, miR-16, and miR-21 have been identified to modulate tumorigenesis through impacting tumor invasion and apoptosis. In this review article, we have a look at the recent advances on our knowledge about the role of miRNAs in human brain development and functions, neurodegenerative diseases, and their clinical potentials.

ConF: A Deep Learning Model Based on BiLSTM, CNN, and Cross Multi-Head Attention Mechanism for Noncoding RNA Family Prediction

This paper presents ConF, a novel deep learning model designed for accurate and efficient prediction of noncoding RNA families. NcRNAs are essential functional RNA molecules involved in various cellular processes, including replication, transcription, and gene expression. Identifying ncRNA families is crucial for comprehensive RNA research, as ncRNAs within the same family often exhibit similar functionalities. Traditional experimental methods for identifying ncRNA families are time-consuming and labor-intensive. Computational approaches relying on annotated secondary structure data face limitations in handling complex structures like pseudoknots and have restricted applicability, resulting in suboptimal prediction performance. To overcome these challenges, ConF integrates mainstream techniques such as residual networks with dilated convolutions and cross multi-head attention mechanisms. By employing a combination of dual-layer convolutional networks and BiLSTM, ConF effectively captures intricate features embedded within RNA sequences. This feature extraction process leads to significantly improved prediction accuracy compared to existing methods. Experimental evaluations conducted using a single, publicly available dataset and applying ten-fold cross-validation demonstrate the superiority of ConF in terms of accuracy, sensitivity, and other performance metrics. Overall, ConF represents a promising solution for accurate and efficient ncRNA family prediction, addressing the limitations of traditional experimental and computational methods.

Elsayed A, López-Berestein G, Amero P, Rodríguez-Aguayo C. An Overview of the Immune Modulatory Properties of Long Non-Coding RNAs and Their Potential Use as Therapeutic Targets in Cancer

Long non-coding RNAs (lncRNAs) play pivotal roles in regulating immune responses, immune cell differentiation, activation, and inflammatory processes. In cancer, they are gaining prominence as potential therapeutic targets due to their ability to regulate immune checkpoint molecules and immune-related factors, suggesting avenues for bolstering anti-tumor immune responses. Here, we explore the mechanistic insights into lncRNA-mediated immune modulation, highlighting their impact on immunity. Additionally, we discuss their potential to enhance cancer immunotherapy, augmenting the effectiveness of immune checkpoint inhibitors and adoptive T cell therapies. LncRNAs as therapeutic targets hold the promise of revolutionizing cancer treatments, inspiring further research in this field with substantial clinical implications.

AP003352.1/miR-141-3p axis enhances the proliferation of osteosarcoma by LPAR3

Osteosarcoma (OS) is a highly malignant tumor with a poor prognosis and a growing incidence. LncRNAs and microRNAs control the occurrence and development process of osteosarcoma through ceRNA patterns. The LPAR3 gene is important in cancer cell proliferation, apoptosis and disease development. However, the regulatory mechanism of the ceRNA network through which LPAR3 participates in osteosarcoma has not been clarified. Herein, our study demonstrated that the AP003352.1/miR-141-3p axis drives LPAR3 expression to induce the malignant progression of osteosarcoma. First, the expression of LPAR3 is regulated by the changes in AP003352.1 and miR-141-3p. Similar to the ceRNA of miR-141-3p, AP003352.1 regulates the expression of LPAR3 through this mechanism. In addition, the regulation of AP003352.1 in malignant osteosarcoma progression depends to a certain degree on miR-141-3p. Importantly, the AP003352.1/miR-141-3p/LPAR3 axis can better serve as a multi-gene diagnostic marker for osteosarcoma. In conclusion, our research reveals a new ceRNA regulatory network, which provides a novel potential target for the diagnosis and treatment of osteosarcoma.

ncDENSE: a novel computational method based on a deep learning framework for non-coding RNAs family prediction

BACKGROUND

Although research on non-coding RNAs (ncRNAs) is a hot topic in life sciences, the functions of numerous ncRNAs remain unclear. In recent years, researchers have found that ncRNAs of the same family have similar functions, therefore, it is important to accurately predict ncRNAs families to identify their functions. There are several methods available to solve the prediction problem of ncRNAs family, whose main ideas can be divided into two categories, including prediction based on the secondary structure features of ncRNAs, and prediction according to sequence features of ncRNAs. The first type of prediction method requires a complicated process and has a low accuracy in obtaining the secondary structure of ncRNAs, while the second type of method has a simple prediction process and a high accuracy, but there is still room for improvement. The existing methods for ncRNAs family prediction are associated with problems such as complicated prediction processes and low accuracy, in this regard, it is necessary to propose a new method to predict the ncRNAs family more perfectly.

RESULTS

A deep learning model-based method, ncDENSE, was proposed in this study, which predicted ncRNAs families by extracting ncRNAs sequence features. The bases in ncRNAs sequences were encoded by one-hot coding and later fed into an ensemble deep learning model, which contained the dynamic bi-directional gated recurrent unit (Bi-GRU), the dense convolutional network (DenseNet), and the Attention Mechanism (AM). To be specific, dynamic Bi-GRU was used to extract contextual feature information and capture long-term dependencies of ncRNAs sequences. AM was employed to assign different weights to features extracted by Bi-GRU and focused the attention on information with greater weights. Whereas DenseNet was adopted to extract local feature information of ncRNAs sequences and classify them by the full connection layer. According to our results, the ncDENSE method improved the Accuracy, Sensitivity, Precision, F-score, and MCC by 2.08[Formula: see text], 2.33[Formula: see text], 2.14[Formula: see text], 2.16[Formula: see text], and 2.39[Formula: see text], respectively, compared with the suboptimal method.

CONCLUSIONS

Overall, the ncDENSE method proposed in this paper extracts sequence features of ncRNAs by dynamic Bi-GRU and DenseNet and improves the accuracy in predicting ncRNAs family and other data.

Beyond the Primary Structure of Nucleic Acids: Potential Roles of Epigenetics and Noncanonical Structures in the Regulations of Plant Growth and Stress Responses

Epigenetics deals with changes in gene expression that are not caused by modifications in the primary sequence of nucleic acids. These changes beyond primary structures of nucleic acids not only include DNA/RNA methylation, but also other reversible conversions, together with histone modifications or RNA interference. In addition, under particular conditions (such as specific ion concentrations or protein-induced stabilization), the right-handed double-stranded DNA helix (B-DNA) can form noncanonical structures commonly described as "non-B DNA" structures. These structures comprise, for example, cruciforms, i-motifs, triplexes, and G-quadruplexes. Their formation often leads to significant differences in replication and transcription rates. Noncanonical RNA structures have also been documented to play important roles in translation regulation and the biology of noncoding RNAs. In human and animal studies, the frequency and dynamics of noncanonical DNA and RNA structures are intensively investigated, especially in the field of cancer research and neurodegenerative diseases. In contrast, noncanonical DNA and RNA structures in plants have been on the fringes of interest for a long time and only a few studies deal with their formation, regulation, and physiological importance for plant stress responses. Herein, we present a review focused on the main fields of epigenetics in plants and their possible roles in stress responses and signaling, with special attention dedicated to noncanonical DNA and RNA structures.

Prokaryotic ncRNAs: Master regulators of gene expression

ncRNA plays a very pivotal role in various biological activities ranging from gene regulation to controlling important developmental networks. It is imperative to note that this small molecule is not only present in all three domains of cellular life, but is an important modulator of gene regulation too in all these domains. In this review, we discussed various aspects of ncRNA biology, especially their role in bacteria. The last two decades of scientific research have proved that this molecule plays an important role in the modulation of various regulatory pathways in bacteria including the adaptive immune system and gene regulation. It is also very surprising to note that this small molecule is also employed in various processes related to the pathogenicity of virulent microorganisms.

Key players in regulatory RNA realm of bacteria

Precise regulation of gene expression is crucial for living cells to adapt for survival in diverse environmental conditions. Among the common cellular regulatory mechanisms, RNA-based regulators play a key role in all domains of life. Discovery of regulatory RNAs have made a paradigm shift in molecular biology as many regulatory functions of RNA have been identified beyond its canonical roles as messenger, ribosomal and transfer RNA. In the complex regulatory RNA network, riboswitches, small RNAs, and RNA thermometers can be identified as some of the key players. Herein, we review the discovery, mechanism, and potential therapeutic use of these classes of regulatory RNAs mainly found in bacteria. Being highly adaptive organisms that inhabit a broad range of ecological niches, bacteria have adopted tight and rapid-responding gene regulation mechanisms. This review aims to highlight how bacteria utilize versatile RNA structures and sequences to build a sophisticated gene regulation network.

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The three major classes of prokaryotic ncRNAs and their characterized mechanisms of operation in gene regulation.

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sRNAs emerging as major players in global gene regulatory networks.

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Riboswitch mediated gene control mechanisms through on/off switches in response to ligand binding.

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RNA thermo sensors for temperature-dependent gene expression.

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Therapeutic importance of ncRNAs and computational approaches involved in the discovery of ncRNAs.

Novel Diagnostic Biomarkers in Colorectal Cancer

Colorectal cancer (CRC) is still a leading cause of cancer death worldwide. Less than half of cases are diagnosed when the cancer is locally advanced. CRC is a heterogenous disease associated with a number of genetic or somatic mutations. Diagnostic markers are used for risk stratification and early detection, which might prolong overall survival. Nowadays, the widespread use of semi-invasive endoscopic methods and feacal blood tests characterised by suboptimal accuracy of diagnostic results has led to the detection of cases at later stages. New molecular noninvasive tests based on the detection of CRC alterations seem to be more sensitive and specific then the current methods. Therefore, research aiming at identifying molecular markers, such as DNA, RNA and proteins, would improve survival rates and contribute to the development of personalized medicine. The identification of “ideal” diagnostic biomarkers, having high sensitivity and specificity, being safe, cheap and easy to measure, remains a challenge. The purpose of this review is to discuss recent advances in novel diagnostic biomarkers for tumor tissue, blood and stool samples in CRC patients.

Detection and Quantification of Small Noncoding RNAs in Marine Diatoms

Endogenous small noncoding RNAs (sRNAs) are a large family of essential regulators of gene expression in both eukaryotes and prokaryotes. Various types of sRNAs with different size and mapping to different genome locations have been recently identified in diatoms, a successful group of phytoplankton in the marine environment. However, their biogenesis and regulatory function are still largely unknown and unexplored in these microalgae, also due to the lack of methods for their experimental analysis. Herein, we present a point-by-point description of the protocols for detection and quantification of sRNAs by Northern-blot analysis and quantitative stem-loop RT-PCR, established in the diatom molecular model specie Phaeodactylum tricornutum.

Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs

Apart from protein-coding Ribonucleic acids (RNAs), there exists a variety of non-coding RNAs (ncRNAs) which regulate complex cellular and molecular processes. High-throughput sequencing technologies and bioinformatics approaches have largely promoted the exploration of ncRNAs which revealed their crucial roles in gene regulation, miRNA binding, protein interactions, and splicing. Furthermore, ncRNAs are involved in the development of complicated diseases like cancer. Categorization of ncRNAs is essential to understand the mechanisms of diseases and to develop effective treatments. Sub-cellular localization information of ncRNAs demystifies diverse functionalities of ncRNAs. To date, several computational methodologies have been proposed to precisely identify the class as well as sub-cellular localization patterns of RNAs). This paper discusses different types of ncRNAs, reviews computational approaches proposed in the last 10 years to distinguish coding-RNA from ncRNA, to identify sub-types of ncRNAs such as piwi-associated RNA, micro RNA, long ncRNA, and circular RNA, and to determine sub-cellular localization of distinct ncRNAs and RNAs. Furthermore, it summarizes diverse ncRNA classification and sub-cellular localization determination datasets along with benchmark performance to aid the development and evaluation of novel computational methodologies. It identifies research gaps, heterogeneity, and challenges in the development of computational approaches for RNA sequence analysis. We consider that our expert analysis will assist Artificial Intelligence researchers with knowing state-of-the-art performance, model selection for various tasks on one platform, dominantly used sequence descriptors, neural architectures, and interpreting inter-species and intra-species performance deviation.

The stacking strategy-based hybrid framework for identifying non-coding RNAs

With the development of next-generation sequencing technology, a large number of transcripts need to be analyzed, and it has been a challenge to distinguish non-coding ribonucleic acid (RNAs) (ncRNAs) from coding RNAs. And for non-model organisms, due to the lack of transcriptional data, many existing methods cannot identify them. Therefore, in addition to using deoxyribonucleic acid-based and RNA-based features, we also proposed a hybrid framework based on the stacking strategy to identify ncRNAs, and we innovatively added eight features based on predicted peptides. The proposed framework was based on stacking two-layer classifier which combined random forest (RF), LightGBM, XGBoost and logistic regression (LR) models. We used this framework to build two types of models. For cross-species ncRNAs identification model, we tested it on six different species: human, mouse, zebrafish, fruit fly, worm and Arabidopsis. Compared with other tools, our model was the best in datasets of Arabidopsis, worm and zebrafish with the accuracy of 98.36%, 99.65% and 94.12%. For performance metrics analysis, the datasets of the six species were considered as a whole set, and the sensitivity, accuracy, precision and F1 values of our model were the best. For the plant-specific ncRNAs identification model, the average values of the six metrics of the two experiments were all greater than 95%, which demonstrated it can be used to identify ncRNAs in plants. The above indicates that the hybrid framework we designed is universal between animals and plants and has significant advantages in the identification of cross-species ncRNAs.

Deep forest ensemble learning for classification of alignments of non-coding RNA sequences based on multi-view structure representations

Non-coding RNAs (ncRNAs) play crucial roles in multiple biological processes. However, only a few ncRNAs’ functions have been well studied. Given the significance of ncRNAs classification for understanding ncRNAs’ functions, more and more computational methods have been introduced to improve the classification automatically and accurately. In this paper, based on a convolutional neural network and a deep forest algorithm, multi-grained cascade forest (GcForest), we propose a novel deep fusion learning framework, GcForest fusion method (GCFM), to classify alignments of ncRNA sequences for accurate clustering of ncRNAs. GCFM integrates a multi-view structure feature representation including sequence-structure alignment encoding, structure image representation and shape alignment encoding of structural subunits, enabling us to capture the potential specificity between ncRNAs. For the classification of pairwise alignment of two ncRNA sequences, the F-value of GCFM improves 6% than an existing alignment-based method. Furthermore, the clustering of ncRNA families is carried out based on the classification matrix generated from GCFM. Results suggest better performance (with 20% accuracy improved) than existing ncRNA clustering methods (RNAclust, Ensembleclust and CNNclust). Additionally, we apply GCFM to construct a phylogenetic tree of ncRNA and predict the probability of interactions between RNAs. Most ncRNAs are located correctly in the phylogenetic tree, and the prediction accuracy of RNA interaction is 90.63%. A web server (http://bmbl.sdstate.edu/gcfm/) is developed to maximize its availability, and the source code and related data are available at the same URL.

RFCM3: Computational Method for Identification of miRNA-mRNA Regulatory Modules in Cervical Cancer

Cervical cancer is a leading severe malignancy throughout the world. Molecular processes and biomarkers leading to tumor progression in cervical cancer are either unknown or only partially understood. An increasing number of studies have shown that microRNAs play an important role in tumorigenesis so understanding the regulatory mechanism of miRNAs in gene-regulatory network will help elucidate the complex biological processes that occur during malignancy. Functional genomics data provides opportunities to study the aberrant microRNA-messenger RNA (miRNA-mRNA) interaction. Identification of miRNA-mRNA regulatory modules will aid deciphering aberrant transcriptional regulatory network in cervical cancer but is computationally challenging. In this regard, an algorithm, termed as relevant and functionally consistent miRNA-mRNA modules (RFCM³), is proposed. It integrates miRNA and mRNA expression data of cervical cancer for identification of potential miRNA-mRNA modules. It selects set of miRNA-mRNA modules by maximizing relation of mRNAs with miRNA and functional similarity between selected mRNAs. Later, using the knowledge of the miRNA-miRNA synergistic network different modules are fused and finally a set of modules are generated containing several miRNAs as well as mRNAs. This type of module explains the underlying biological pathways containing multiple miRNAs and mRNAs. The effectiveness of the proposed approach over other existing methods has been demonstrated on a miRNA and mRNA expression data of cervical cancer with respect to enrichment analyses and other standard metrices. The prognostic value of the genes in a module with respect to cervical cancer is also demonstrated. The approach was found to generate more robust, integrated, and functionally enriched miRNA-mRNA modules in cervical cancer.

Prospective Advances in Non-coding RNAs Investigation

Non-coding RNAs (ncRNAs) play significant roles in numerous physiological cellular processes and molecular alterations during pathological conditions including heart diseases, cancer, immunological disorders and neurological diseases. This chapter is focusing on the basis of ncRNA relation with their functions and prospective advances in non-coding RNAs particularly miRNAs investigation in the cardiovascular disease management.The field of ncRNAs therapeutics is a very fascinating and challenging too. Scientists have opportunity to develop more advanced therapeutics as well as diagnostic approaches for cardiovascular conditions. Advanced studies are critically needed to deepen the understanding of the molecular biology, mechanism and modulation of ncRNAs and chemical formulations for managing CVDs.

Non-Coding RNAs in Psychiatric Disorders and Suicidal Behavior

It is well known that only a small proportion of the human genome code for proteins; the rest belong to the family of RNAs that do not code for protein and are known as non-coding RNAs (ncRNAs). ncRNAs are further divided into two subclasses based on size: 1) long non-coding RNAs (lncRNAs; >200 nucleotides) and 2) small RNAs (<200 nucleotides). Small RNAs contain various family members that include microRNAs (miRNAs), small interfering RNAs (siRNAs), piwi-interacting RNAs (piRNAs), small nucleolar RNAs (snoRNAs), and small nuclear RNAs (snRNAs). The roles of ncRNAs, especially lncRNAs and miRNAs, are well documented in brain development, homeostasis, stress responses, and neural plasticity. It has also been reported that ncRNAs can influence the development of psychiatric disorders including schizophrenia, major depressive disorder, and bipolar disorder. More recently, their roles are being investigated in suicidal behavior. In this article, we have comprehensively reviewed the findings of lncRNA and miRNA expression changes and their functions in various psychiatric disorders including suicidal behavior. We primarily focused on studies that have been done in postmortem human brain. In addition, we have briefly reviewed the role of other small RNAs (e.g. piwiRNA, siRNA, snRNA, and snoRNAs) and their expression changes in psychiatric illnesses.

Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

BACKGROUND

Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.

OBJECTIVE

The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA's roles in cellular processes and drugs design, briefly.

METHOD

In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.

RESULTS

The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.

CONCLUSION

ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

Rational in silico design of aptamers for organophosphates based on the example of paraoxon

Poisoning by organophosphates (OPs) takes one of the leading places in the total number of exotoxicoses. Detoxication of OPs at the first stage of the poison entering the body could be achieved with the help of DNA- or RNA-aptamers, which are able to bind poisons in the bloodstream. The aim of the research was to develop an approach to rational in silico design of aptamers for OPs based on the example of paraoxon. From the published sequence of an aptamer binding organophosphorus pesticides, its three-dimensional model has been constructed. The most probable binding site for paraoxon was determined by molecular docking and molecular dynamics (MD) methods. Then the nucleotides of the binding site were mutated consequently and the values of free binding energy have been calculated using MD trajectories and MM-PBSA approach. On the basis of the energy values, two sequences that bind paraoxon most efficiently have been selected. The value of free binding energy of paraoxon with peripheral anionic site of acetylcholinesterase (AChE) has been calculated as well. It has been revealed that the aptamers found bind paraoxon more effectively than AChE. The peculiarities of paraoxon interaction with the aptamers nucleotides have been analyzed. The possibility of improving in silico approach for aptamer selection is discussed.

Volatile Evolution of Long Non-Coding RNA Repertoire in Retinal Pigment Epithelium: Insights from Comparison of Bovine and Human RNA Expression Profiles

Currently, several long non-coding RNAs (lncRNAs) (TUG1, MALAT1, MEG3 and others) have been discovered to regulate normal visual function and may potentially contribute to dysfunction of the retina. We decided to extend these analyses of lncRNA genes to the retinal pigment epithelium (RPE) to determine whether there is conservation of RPE-expressed lncRNA between human and bovine genomes. We reconstructed bovine RPE lncRNAs based on genome-guided assembly. Next, we predicted homologous human transcripts based on whole genome alignment. We found a small set of conserved lncRNAs that could be involved in signature RPE functions that are conserved across mammals. However, the fraction of conserved lncRNAs in the overall pool of lncRNA found in RPE appeared to be very small (less than 5%), perhaps reflecting a fast and flexible adaptation of the mammalian eye to various environmental conditions.

Regulatory sRNAs in Cyanobacteria

As the transcriptional and post-transcriptional regulators of gene expression, small RNAs (sRNAs) play important roles in every domain of life in organisms. It has been discovered gradually that bacteria possess multiple means of gene regulation using RNAs. They have been continuously used as model organisms for photosynthesis, metabolism, biotechnology, evolution, and nitrogen fixation for many decades. Cyanobacteria, one of the most ancient life forms, constitute all kinds of photoautotrophic bacteria and exist in almost any environment on this planet. It is believed that a complex RNA-based regulatory mechanism functions in cyanobacteria to help them adapt to changes and stresses in diverse environments. Although lagging far behind other model microorganisms, such as yeast and Escherichia coli, more and more non-coding regulatory sRNAs have been recognized in cyanobacteria during the past decades. In this article, by focusing on cyanobacterial sRNAs, the approaches for detection and targeting of sRNAs will be summarized, four major mechanisms and regulatory functions will be generalized, eight types of cis-encoded sRNA and four types of trans-encoded sRNAs will be reviewed in detail, and their possible physiological functions will be further discussed.

Long Non-Coding RNAs in Gliomas: From Molecular Pathology to Diagnostic Biomarkers and Therapeutic Targets

Gliomas are the most common malignancies of the central nervous system. Because of tumor localization and the biological behavior of tumor cells, gliomas are characterized by very poor prognosis. Despite significant efforts that have gone into glioma research in recent years, the therapeutic efficacy of available treatment options is still limited, and only a few clinically usable diagnostic biomarkers are available. More and more studies suggest non-coding RNAs to be promising diagnostic biomarkers and therapeutic targets in many cancers, including gliomas. One of the largest groups of these molecules is long non-coding RNAs (lncRNAs). LncRNAs show promising potential because of their unique tissue expression patterns and regulatory functions in cancer cells. Understanding the role of lncRNAs in gliomas may lead to discovery of the novel molecular mechanisms behind glioma biological features. It may also enable development of new solutions to overcome the greatest obstacles in therapy of glioma patients. In this review, we summarize the current knowledge about lncRNAs and their involvement in the molecular pathology of gliomas. A conclusion follows that these RNAs show great potential to serve as powerful diagnostic, prognostic, and predictive biomarkers as well as therapeutic targets.

Bacterial regulatory RNAs: complexity, function, and putative drug targeting

Over the past decade, RNA-deep sequencing has uncovered copious non-protein coding RNAs (npcRNAs) in bacteria. Many of them are key players in the regulation of gene expression, taking part in various regulatory circuits, such as metabolic responses to different environmental stresses, virulence, antibiotic resistance, and host-pathogen interactions. This has contributed to the high adaptability of bacteria to changing or even hostile environments. Their mechanisms include the regulation of transcriptional termination, modulation of translation, and alteration of messenger RNA (mRNA) stability, as well as protein sequestration. Here, the mechanisms of gene expression by regulatory bacterial npcRNAs are comprehensively reviewed and supplemented with well-characterized examples. This class of molecules and their mechanisms of action might be useful targets for the development of novel antibiotics.

Assessment of Bona Fide sRNAs in Staphylococcus aureus

Bacterial regulatory RNAs have been extensively studied for over a decade, and are progressively being integrated into the complex genetic regulatory network. Transcriptomic arrays, recent deep-sequencing data and bioinformatics suggest that bacterial genomes produce hundreds of regulatory RNAs. However, while some have been authenticated, the existence of the others varies according to strains and growth conditions, and their detection fluctuates with the methodologies used for data acquisition and interpretation. For example, several small RNA (sRNA) candidates are now known to be parts of UTR transcripts. Accurate annotation of regulatory RNAs is a complex task essential for molecular and functional studies. We defined bona fide sRNAs as those that (i) likely act in trans and (ii) are not expressed from the opposite strand of a coding gene. Using published data and our own RNA-seq data, we reviewed hundreds of Staphylococcus aureus putative regulatory RNAs using the DETR'PROK computational pipeline and visual inspection of expression data, addressing the question of which transcriptional signals correspond to sRNAs. We conclude that the model strain HG003, a NCTC8325 derivative commonly used for S. aureus genetic regulation studies, has only about 50 bona fide sRNAs, indicating that these RNAs are less numerous than commonly stated. Among them, about half are associated to the S. aureus sp. core genome and a quarter are possibly expressed in other Staphylococci. We hypothesize on their features and regulation using bioinformatic approaches.

Noncoding RNAs: Master Regulators of Inflammatory Signaling

Inflammatory signaling underlies many diseases, from arthritis to cancer. Our understanding of inflammation has thus far been limited to the world of proteins, because we are only just beginning to understand the role that noncoding RNAs (ncRNA) might play. It is now clear that ncRNA do not constitute transcriptional 'noise' but instead harbor physiological functions in controlling signaling pathways. In this review, we cover the newly discovered mechanisms and functions of ncRNAs in the regulation of inflammatory signaling. We also describe advances in experimental techniques allowing this field of research to take root. These findings have opened new avenues for putative therapeutic intervention in inflammatory diseases, which may be seen translated into clinical outcomes in the future.

Therapeutic Approaches and Role of ncRNAs in Cardiovascular Disorders and Insulin Resistance

Diseases resulting from alterations in gene expressions through mutations in the genes or through changes in the gene expression regulation could be identified through the analysis of RNA expressions. ncRNAs play a significant role in regulation of the gene expression by controlling the expression levels of the coding RNAs and other cellular processes. Discoveries have shown that the human genome is encoded with sequences responsible for the transcription of thousands of ncRNAs. Even though the studies conducted on ncRNAs are still at initial stages, facts established so far display biomarkers that confirm their relationship with certain diseases such as cancers, cardiovascular diseases, and insulin resistance. These studies have been facilitated with high throughput modern sequencing techniques such as microarrays and RNA sequencing. The data obtained through the above analysis are processed with the aid of existing databases, to deduce conclusions on different diagnostic biomarkers and therapeutic targets for specific diseases. This review focuses on the association of ncRNAs in disease prediction, focusing mainly on cardiovascular diseases and disorders caused by insulin resistance. The report also analyzes regulatory functions of ncRNAs and novel approaches used in disease therapeutics.

Genome-Wide Detection of Small Regulatory RNAs in Deep-Sea Bacterium Shewanella piezotolerans WP3

Shewanella are one of the most abundant Proteobacteria in the deep-sea and are renowned for their versatile electron accepting capacities. The molecular mechanisms involved in their adaptation to diverse and extreme environments are not well understood. Small non-coding RNAs (sRNAs) are known for modulating the gene expression at transcriptional and posttranscriptional levels, subsequently playing a key role in microbial adaptation. To understand the potential roles of sRNAs in the adaptation of Shewanella toward deep-sea environments, here an in silico approach was utilized to detect the sRNAs in the genome of Shewanella piezotolerans WP3, a piezotolerant and psychrotolerant deep-sea iron reducing bacterium. After scanning 3673 sets of 5' and 3' UTRs of orthologous genes, 209 sRNA candidates were identified with high confidence in S. piezotolerans WP3. About 92% (193 out of 209) of these putative sRNAs belong to the class trans-encoded RNAs, suggesting that trans-regulatory RNAs are the dominant class of sRNAs in S. piezotolerans WP3. The remaining 16 cis-regulatory RNAs were validated through quantitative polymerase chain reaction. Five cis-sRNAs were further shown to act as cold regulated sRNAs. Our study provided additional evidence at the transcriptional level to decipher the microbial adaptation mechanisms to extreme environmental conditions.

A Review on Recent Computational Methods for Predicting Noncoding RNAs

Noncoding RNAs (ncRNAs) play important roles in various cellular activities and diseases. In this paper, we presented a comprehensive review on computational methods for ncRNA prediction, which are generally grouped into four categories: (1) homology-based methods, that is, comparative methods involving evolutionarily conserved RNA sequences and structures, (2) de novo methods using RNA sequence and structure features, (3) transcriptional sequencing and assembling based methods, that is, methods designed for single and pair-ended reads generated from next-generation RNA sequencing, and (4) RNA family specific methods, for example, methods specific for microRNAs and long noncoding RNAs. In the end, we summarized the advantages and limitations of these methods and pointed out a few possible future directions for ncRNA prediction. In conclusion, many computational methods have been demonstrated to be effective in predicting ncRNAs for further experimental validation. They are critical in reducing the huge number of potential ncRNAs and pointing the community to high confidence candidates. In the future, high efficient mapping technology and more intrinsic sequence features (e.g., motif and k-mer frequencies) and structure features (e.g., minimum free energy, conserved stem-loop, or graph structures) are suggested to be combined with the next- and third-generation sequencing platforms to improve ncRNA prediction.

Novel miRNA-mRNA interactions conserved in essential cancer pathways

Cancer is a complex disease in which unrestrained cell proliferation results in tumour development. Extensive research into the molecular mechanisms underlying tumorigenesis has led to the characterization of oncogenes and tumour suppressors that are key elements in cancer growth and progression, as well as that of other important elements like microRNAs. These genes and miRNAs appear to be constitutively deregulated in cancer. To identify signatures of miRNA-mRNA interactions potentially conserved in essential cancer pathways, we have conducted an integrative analysis of transcriptomic data, also taking into account methylation and copy number alterations. We analysed 18,605 raw transcriptome samples from The Cancer Genome Atlas covering 15 of the most common types of human tumours. From this global transcriptome study, we recovered known cancer-associated miRNA-targets and importantly, we identified new potential targets from miRNA families, also analysing the phenotypic outcomes of these genes/mRNAs in terms of survival. Further analyses could lead to novel approaches in cancer therapy.

Evidence of putative non-coding RNAs from Leishmania untranslated regions

Non-coding RNAs (ncRNAs) are regulatory elements present in a wide range of organisms, including trypanosomatids. ncRNAs transcribed from the untranslated regions (UTRs) of coding genes have been described in the transcriptomes of several eukaryotes, including Trypanosoma brucei. To uncover novel putative ncRNAs in two Leishmania species, we examined a L. major cDNA library and a L. donovani non-polysomal RNA library. Using a combination of computational analysis and experimental approaches, we classified 26 putative ncRNA in L. major, of these, 5 arising from intergenic regions and 21 from untranslated regions. In L. donovani, we classified 37 putative ncRNAs, of these, 7 arising from intergenic regions, and 30 from UTRs. Our results suggest, for the first time, that UTR-transcripts may be a common feature in the eukaryote Leishmania similarly to those previously shown in T. brucei and other eukaryotes.

Small Antisense RNA RblR Positively Regulates RuBisCo in Synechocystis sp. PCC 6803

Small regulatory RNAs (sRNAs) function as transcriptional and post-transcriptional regulators of gene expression in organisms from all domains of life. Cyanobacteria are thought to have developed a complex RNA-based regulatory mechanism. In the current study, by genome-wide analysis of differentially expressed small RNAs in Synechocystis sp. PCC 6803 under high light conditions, we discovered an asRNA (RblR) that is 113nt in length and completely complementary to its target gene rbcL, which encodes the large chain of RuBisCO, the enzyme that catalyzes carbon fixation. Further analysis of the RblR(+)/(−) mutants revealed that RblR acts as a positive regulator of rbcL under various stress conditions; Suppressing RblR adversely affects carbon assimilation and thus the yield, and those phenotypes of both the wild type and the overexpressor could be downgraded to the suppressor level by carbonate depletion, indicated a regulatory role of RblR in CO₂ assimilation. In addition, a real-time expression platform in Escherichia coli was setup and which confirmed that RblR promoted the translation of the rbcL mRNA into the RbcL protein. The present study is the first report of a regulatory RNA that targets RbcL in Synechocystis sp. PCC 6803, and provides strong evidence that RblR regulates photosynthesis by positively modulating rbcL expression in Synechocystis.

Prediction of miRNA-mRNA Interactions Using miRGate

miRGate ( http://mirgate.bioinfo.cnio.es /) is a freely available database that contains predicted and experimentally validated microRNA-messenger RNA (miRNA-mRNA) target pairs. This resource includes novel predictions from five well-established algorithms, but recalculated from a common and comprehensive sequence dataset. It includes all 3'-UTR sequences of all known genes of the three more widely employed genomes (human, mouse, and rat), and all annotated miRNA sequences from those genomes. Besides, it also contains predictions for all genes in human targeted by miRNA viruses such as Epstein-Barr and Kaposi sarcoma-associated herpes virus.The approach intends to circumvent one of the main drawbacks in this area, as diverse sequences and gene database versions cause poor overlap among different target prediction methods even with experimentally confirmed targets. As a result, miRGate predictions have been successfully validated using functional assays in several laboratories.This chapter describes how a user can access target information via miRGate's web interface. It also shows how automatically access the database through the programmatic interface based on representational state transfer services (REST), using the application programming interface (API) available at http://mirgate.bioinfo.cnio.es/API .

RNAlien - Unsupervised RNA family model construction

Determining the function of a non-coding RNA requires costly and time-consuming wet-lab experiments. For this reason, computational methods which ascertain the homology of a sequence and thereby deduce functionality and family membership are often exploited. In this fashion, newly sequenced genomes can be annotated in a completely computational way. Covariance models are commonly used to assign novel RNA sequences to a known RNA family. However, to construct such models several examples of the family have to be already known. Moreover, model building is the work of experts who manually edit the necessary RNA alignment and consensus structure. Our method, RNAlien, starting from a single input sequence collects potential family member sequences by multiple iterations of homology search. RNA family models are fully automatically constructed for the found sequences. We have tested our method on a subset of the Rfam RNA family database. RNAlien models are a starting point to construct models of comparable sensitivity and specificity to manually curated ones from the Rfam database. RNAlien Tool and web server are available at http://rna.tbi.univie.ac.at/rnalien/.

Transcriptome sequencing analysis of novel sRNAs of Kineococcus radiotolerans in response to ionizing radiation

Kineococcus radiotolerans is a Gram-positive, radio-resistant bacterium isolated from a radioactive environment. The small noncoding RNAs (sRNAs) in bacteria are reported to play roles in the immediate response to stress and/or the recovery from stress. The analysis of K. radiotolerans transcriptome sequencing results can identify these sRNAs in a genome-wide detection, using RNA sequencing (RNA-seq) by the deep sequencing technique. In this study, the raw data of radiation-exposed samples (RS) and control samples (CS) were acquired separately from the sequencing platform. There were 217 common sRNA candidates in the two samples screened in the genome-wide scale by bioinformatics analysis. There were 43 differentially expressed sRNA candidates, including 28 up-regulated and 15 down-regulated ones. The down-regulated sRNAs were selected for the sRNA target prediction, of which 12 sRNAs that may modulate the genes related to the transcription regulation and DNA repair were considered as the candidates involved in the radio-resistance regulation system.

A New Noncoding RNA Arranges Bacterial Chromosome Organization

Repeated extragenic palindromes (REPs) in the enterobacterial genomes are usually composed of individual palindromic units separated by linker sequences. A total of 355 annotated REPs are distributed along the Escherichia coli genome. RNA sequence (RNAseq) analysis showed that almost 80% of the REPs in E. coli are transcribed. The DNA sequence of REP₃₂₅ showed that it is a cluster of six repeats, each with two palindromic units capable of forming cruciform structures in supercoiled DNA. Here, we report that components of the REP₃₂₅ element and at least one of its RNA products play a role in bacterial nucleoid DNA condensation. These RNA not only are present in the purified nucleoid but bind to the bacterial nucleoid-associated HU protein as revealed by RNA IP followed by microarray analysis (RIP-Chip) assays. Deletion of REP₃₂₅ resulted in a dramatic increase of the nucleoid size as observed using transmission electron microscopy (TEM), and expression of one of the REP₃₂₅ RNAs, nucleoid-associated noncoding RNA 4 (naRNA4), from a plasmid restored the wild-type condensed structure. Independently, chromosome conformation capture (3C) analysis demonstrated physical connections among various REP elements around the chromosome. These connections are dependent in some way upon the presence of HU and the REP₃₂₅ element; deletion of HU genes and/or the REP₃₂₅ element removed the connections. Finally, naRNA4 together with HU condensed DNA in vitro by connecting REP₃₂₅ or other DNA sequences that contain cruciform structures in a pairwise manner as observed by atomic force microscopy (AFM). On the basis of our results, we propose molecular models to explain connections of remote cruciform structures mediated by HU and naRNA4.

Nucleoid organization in bacteria is being studied extensively, and several models have been proposed. However, the molecular nature of the structural organization is not well understood. Here we characterized the role of a novel nucleoid-associated noncoding RNA, naRNA4, in nucleoid structures both in vivo and in vitro. We propose models to explain how naRNA4 together with nucleoid-associated protein HU connects remote DNA elements for nucleoid condensation. We present the first evidence of a noncoding RNA together with a nucleoid-associated protein directly condensing nucleoid DNA.

Long Non-Coding RNAs: The Key Players in Glioma Pathogenesis

Long non-coding RNAs (LncRNAs) represent a novel class of RNAs with no functional protein-coding ability, yet it has become increasingly clear that interactions between lncRNAs with other molecules are responsible for important gene regulatory functions in various contexts. Given their relatively high expressions in the brain, lncRNAs are now thought to play important roles in normal brain development as well as diverse disease processes including gliomagenesis. Intriguingly, certain lncRNAs are closely associated with the initiation, differentiation, progression, recurrence and stem-like characteristics in glioma, and may therefore be exploited for the purposes of sub-classification, diagnosis and prognosis. LncRNAs may also serve as potential therapeutic targets as well as a novel biomarkers in the treatment of glioma. In this article, the functional aspects of lncRNAs, particularly within the central nervous system (CNS), will be briefly discussed, followed by highlights of the important roles of lncRNAs in mediating critical steps during glioma development. In addition, the key lncRNA players and their possible mechanistic pathways associated with gliomagenesis will be addressed.

Specificity Analysis of Histone Modification-Specific Antibodies or Reading Domains on Histone Peptide Arrays

Histone posttranslational modifications (PTMs) have a crucial role in chromatin regulation and dynamics. They are specifically bound by so-called reading domains, which mediate the biological effects of histone PTMs. On a similar note, antibodies are invaluable reagents in chromatin biology for the detection, characterization, and mapping of histone PTMs. Despite these central roles in chromatin research and biology, the specificity of many antibodies and reading domains has been insufficiently characterized and documented. Here we describe in detail the application of the MODified™ Histone Peptide Array for the investigation of the binding specificity of histone binding antibodies or domains. The array contains 384 histone tail peptides carrying 59 posttranslational modifications in different combinations which can be used to study the primary binding specificity, but at the same time also allow to determine the combinatorial effect of secondary marks on antibody or reading domain binding.